1. Field of the Invention
The present invention relates to a system for detecting an object from an image and a method therefor.
2. Description of the Related Art
In recent years, there has been a system which detects a person from an image captured by a monitoring camera to use the result of detection to detect an intruder, monitor the action of the intruder, and monitor degree of congestion of persons. A method for detecting a person applicable to such a system has been discussed (refer to Dalal and Triggs, “Histograms of Oriented Gradients for Human Detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (VPR2005), (Document 1)). This method extracts a histogram in the gradient direction of a pixel value from an image to determine whether a partial area in the image is a person using the histogram as a feature quantity.
Other than that, there has been discussed a method for detecting a human body at a high speed such that AdaBoost learning is performed handling the feature amount used in the Document 1 as a weak discriminator, and a cascade discriminator is executed based on the AdaBoost learning (refer to Qiang Zhu et al, “Fast human detection using a cascade of Histograms of Oriented Gradients,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR2006), (Document 2)).
On the other hand, as to the monitoring camera it is desired to detect a small person in the distance, so that an image is increased in resolution.
However, the detection of a small person from a high-resolution image enormously increases throughput, which increases time for processing one frame among monitoring images. This causes a problem that a person quickly moves in a screen cannot be detected.